Automatic Discovery of Object-Centric Behavioral Constraint Models

2017 ◽  
pp. 43-58 ◽  
Author(s):  
Guangming Li ◽  
Renata Medeiros de Carvalho ◽  
Wil M. P. van der Aalst
Keyword(s):  
Automatic Discovery ◽  
Behavioral Constraint

Object-centric behavioral constraint models

2019 ◽  
Author(s):  
Guangming Li ◽  
Renata Medeiros de Carvalho ◽  
Wil M. P. van der Aalst
Keyword(s):  
Behavioral Constraint

scholarly journals Towards Automatic Data Format Transformations: Data Wrangling at Scale

2018 ◽  
Vol 62 (7) ◽  
pp. 1044-1060 ◽  
Author(s):  
Alex Bogatu ◽  
Norman W Paton ◽  
Alvaro A A Fernandes ◽  
Martin Koehler
Keyword(s):  
Analysis Data ◽  
Data Sources ◽  
Open Government ◽  
Tool Support ◽  
Data Format ◽  
Automatic Data ◽  
Open Government Data ◽  
Automatic Discovery ◽  
Numerous Data ◽  
Government Data

Abstract Data wrangling is the process whereby data are cleaned and integrated for analysis. Data wrangling, even with tool support, is typically a labour intensive process. One aspect of data wrangling involves carrying out format transformations on attribute values, for example so that names or phone numbers are represented consistently. Recent research has developed techniques for synthesizing format transformation programs from examples of the source and target representations. This is valuable, but still requires a user to provide suitable examples, something that may be challenging in applications in which there are huge datasets or numerous data sources. In this paper, we investigate the automatic discovery of examples that can be used to synthesize format transformation programs. In particular, we propose two approaches to identifying candidate data examples and validating the transformations that are synthesized from them. The approaches are evaluated empirically using datasets from open government data.

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